The assertion that data warehouses are obsolete, and data lakes are the definitive solution is an oversimplification. Both technologies serve distinct
purposes, and their effectiveness depends on specific organizational needs and data characteristics. However, the need for data lakes has reached centre stage in banking due to a number of drivers including –
Growth of partnership-based models like API Banking, Co-lending, & Neo Banking, driving the need for real-time data integration, open banking ecosystems, and interoperability.
Shift from Single Core Banking System Model to a Multi-Core Banking Architecture, demanding scalable and flexible data platforms, with an emphasis on data harmonization, multi-system data integration, and distributed data management.
Growth of unstructured data sources such as files, images, and logs that require advanced data ingestion, AI-powered data processing, and data lakes for handling semi-structured and unstructured data effectively.
Regulatory & Operational reporting needs have exploded, requiring access to real-time data streams, granular data visibility, and compliance-driven data management.
In light of these new age needs, data warehouses pose a range of challenges including –
Rigidity: Data warehouses require a predefined schema, limiting flexibility and making them less adaptable to dynamic, unstructured, and semi-structured data types.
High Costs: Building and maintaining a data warehouse demands significant infrastructure investment, with storage costs escalating as data volumes increase, especially for big data workloads.
Time-consuming: The ETL (Extract, Transform, Load) process in data warehouses is resource-intensive and time-consuming, delaying real-time analytics and data-driven insights.
Limited Scalability: Traditional data warehouses struggle with horizontal scalability, making it difficult to manage exponential data growth without substantial increases in operational complexity and cost.
Data lakes have evolved as a solution to this problem. The architecture of a data lake to support separation of storage from compute has further helped in the migration to data lakes. Traditional data warehouses had tightly coupled Compute and Storage, which limited scalability and flexibility.
Drona Pay’s Data Platform (EDP) solves these problems by using new age storage formats like Apache Iceberg to integrate diverse systems including Core Banking, Card Management Systems, Loan Management Systems, Co-lending Platforms, Treasury, Internet Banking, Mobile Banking, Corporate Banking and Payments. The Drona Pay stack enables scalable data ingestion, processing, storage, governance, and visualization. With this stack, banks can deploy a comprehensive data lake infrastructure across leading cloud platforms or on-premise, offering a future-proof solution for data management.
Drona Pay's Data Platform helps Banks with use cases including Early Warning System (EWS), Regulatory Reporting, Operational Reporting, VaR modelling and Complex What-If modelling.
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